ENDOMETRIAL CANCER ANALYSIS

Emile Cohen

July 2020

Goal: In this notebook, we want to understand what makes Endometrial Cancer a textbook case for the patterns we saw, and what are the major subcohorts that drive the signal.

In [9]:
%run -i '../../../../../utils/setup_environment.ipy'

import warnings
warnings.filterwarnings('ignore')
from scipy.stats import fisher_exact, ranksums, chi2, norm
from statsmodels.sandbox.stats.multicomp import multipletests
import matplotlib.gridspec as gridspec
import pickle

data_path = '../../../../../data/'
data_wgd = data_path + 'impact-facets-tp53/processed/wgd/'
data_no_wgd = data_path + 'impact-facets-tp53/processed/no_wgd/'

Setup environment... done!

✅ Working on **mskimpact_env** conda environment.

In [11]:
# Export to HTML
!jupyter nbconvert --to html endometrial_cancer.ipynb

[NbConvertApp] Converting notebook endometrial_cancer.ipynb to html
[NbConvertApp] Writing 4358704 bytes to endometrial_cancer.html

Interesting functions

In [6]:
from functools import reduce

def get_hotspots(df: pd.DataFrame, Sample_Type: str, group: list = None, group_type:str = None):
    data = df[df['Sample_Type'] == Sample_Type]
    
    if group and group_type:
        data = data[data[group_type].isin(group)]

    data_1 = get_groupby(data,'tp53_spot_1', 'count'); data_2 = get_groupby(data,'tp53_spot_2', 'count'); data_3 = get_groupby(data,'tp53_spot_3', 'count') ; data_4 = get_groupby(data,'tp53_spot_4', 'count') ; data_5 = get_groupby(data,'tp53_spot_5', 'count') 
    series_data = [data_1,data_2,data_3,data_4,data_5]

    df_merged = reduce(lambda  left,right: pd.merge(left,right,left_index=True, right_index=True,
                                                how='outer'), series_data).fillna(0)

    df_merged.columns = ['count_1', 'count_2', 'count_3', 'count_4', 'count_5']
    df_merged['total'] = df_merged.sum(axis=1)
    df_merged = df_merged.sort_values(by='total', ascending=False)

    df_merged = df_merged.drop('nan')
    
    return df_merged

def get_hotspot_frac(df: pd.DataFrame, group_type:str = None, group: list = None, nb = 10):
    if group_type and group:
        df = df[df[group_type].isin(group)]
    result = [['spot', '#', 'frac']]
    for spot in get_groupby(df, 'tp53_spot_1', 'count').sort_values(by='count', ascending=False).head(nb).index.tolist():
        result.append([spot,df[df['tp53_spot_1'] == spot].frac_genome_altered.shape[0], df[df['tp53_spot_1'] == spot].frac_genome_altered.median()])

    return pd.DataFrame(result)


def boxplot_sampletype(df: pd.DataFrame, group:str, palette, order, metrics: str, figsize= (10,3), title: str = '', title_font: int=12, xlim=[0,1]):
    fig=plt.figure(figsize=figsize)
    ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)

    sns.boxplot(y=metrics, x=group,data=df,ax=ax, dodge=False,order=order, palette=palette).set_title(title, weight='bold', fontsize=title_font)
    
    groupby_ = get_groupby(df,group, 'count')
    
    labels = []
    for element in order:
        labels.append(element + '\n('+ str(groupby_.loc[element].values[0])+')')
    
    
    
    ax.set_xticklabels(labels)
    style(ax)
    ax.set_ylim(xlim)
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)

    return fig, ax


# Let's give a look at medians and statistics

def get_statistics(df: pd.DataFrame, group:str, metrics: str, group_list: list):
    group_1 = df[df[group] == group_list[0]][metrics]
    group_2 = df[df[group] == group_list[1]][metrics]

    median_1 = group_1.median()
    median_2 = group_2.median()

    statistic, p_value = ranksums(group_1.dropna().values,group_2.dropna().values)
    
    results = [['', 'size', metrics],
               [group_list[0], group_1.shape[0], median_1],
               [group_list[1], group_2.shape[0], median_2],
               ['', 'Statistics', 'p-value'],
               ['', statistic, p_value]]


    return pd.DataFrame(results)


def get_major_codrivers(master: pd.DataFrame, maf: pd.DataFrame, head:int = 10, tp53=False):
    samples = master.Tumor_Id.tolist()
    if tp53:
        maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True]
    else:
        maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True][maf['Hugo_Symbol'] != 'TP53']
    h = pd.DataFrame(maf_filtered[['Hugo_Symbol']].groupby(['Hugo_Symbol']).size())
    h.columns = ['count']
    h = h.sort_values(by='count', ascending=False).head(head)
    
    return(h)


def create_co_drivers_table(master: pd.DataFrame, group_type:str,  group_1: str, group_2: str):
    master_group_1 = master[master[group_type] == group_1]
    co_drivers_group_1 = get_major_codrivers(master=master_group_1,
                        maf=maf_cohort_nowgd,
                       head=100)
    co_drivers_group_1['proportion_1'] = co_drivers_group_1.apply(lambda x: 100* round(x['count'] / co_drivers_group_1.sum().values[0], 4), axis=1)
    
    master_group_2 = master[master[group_type] == group_2]
    co_drivers_group_2 = get_major_codrivers(master=master_group_2,
                        maf=maf_cohort_nowgd,
                       head=100)
    co_drivers_group_2['proportion_2'] = co_drivers_group_2.apply(lambda x: 100* round(x['count'] / co_drivers_group_2.sum().values[0], 4), axis=1)
    
    co_drivers_groups = pd.merge(co_drivers_group_1, co_drivers_group_2, on='Hugo_Symbol')
    co_drivers_groups['proportion_1'] = - co_drivers_groups['proportion_1']
    
    return co_drivers_groups

Master Definition and Filtering

In [7]:
cancer = 'Endometrial Cancer'
In [8]:
master_no_wgd = non_wgd_load_and_cut(data_path + 'impact-facets-tp53/processed/no_wgd/master_no_wgd.pkl')
master_wgd = pd.read_pickle(data_path + 'impact-facets-tp53/processed/wgd/master_wgd.pkl')

master_no_wgd_cancer = master_no_wgd[master_no_wgd['Cancer_Type'] == cancer]
master_wgd_cancer = master_wgd[master_wgd['Cancer_Type'] == cancer]

maf_cohort_nowgd = pd.read_csv(data_path + 'impact-facets-tp53/processed/no_wgd/maf_cohort_nowgd.txt', sep='\t').drop('Unnamed: 0', axis=1)
maf_cohort_wgd = pd.read_csv(data_path + 'impact-facets-tp53/processed/wgd/maf_cohort_wgd.txt', sep='\t').drop('Unnamed: 0', axis=1)

What makes Endometrial Cancer a TextBook Case?

WGD Proportion

Breast Cancer is the biggest cancer in our cohort. Breast Cancer has a slightly low proportion of WGD - around 30%

Cancer Panel

  • Endometrial Cancer is one of the most represented cancer in MSK-Impact Cohort.
  • Highly enriched in LOSS Samples: 0_HETLOSS and >=1_LOSS account for more than 90% of the cancer cohort
  • Depleted in >=1_cnLOH, 0_HETLOSS

Genome Instability

Endometrial Cancer shows a significant difference in Genome Instability between TP53 Mono-Allelic and Bi-Allelic subgroups - and has a lot of samples in both groups.

In the TP53 subgroup Pan Cancer plot that follows, we can see 2 important signals:

  • 1_WILD_TYPE and 0_HETLOSS GI are very low compared to other subgroups
  • Bi Allelic Subgroups - >=1_LOSS and >=1_cnLOH - have higher GI than other subgroups and the difference is significant

WGD Part

Subgroup Proportion

In the following cells are the proportions of different groupo levels: on the right Primary samples, on the left Metastatic samples.

Very High Genome Instability

In WGD cohort, Genome Instability median is above 70% for all cancer types.

We still see difference between TP53 bi-allelic and mono-allelic states but those are not very significant:

No WGD Part - Cancer Investigation

In this section, our goal is to find subcohorts that lead the signals observed. Here are the different subcohort we will create:

  • Hotspot Analysis: splitting on 273 / 248 / 175 / Missense / Truncated / In Frame
  • CCF Analysis
  • SNV/INDEL Analysis

Hotspot Analysis

In this section, we cut our cohort to only keep samples with exactly one TP53 mutation, for simplicity.

In [12]:
master_hotspot = master_no_wgd_cancer[master_no_wgd_cancer['tp53_count'] == 1]
In [13]:
get_hotspot_frac(df=master_hotspot,
                group_type=None,
                group=None)
Out[13]:
0 1 2
0 spot # frac
1 273 20 0.2905
2 248 16 0.291
3 245 10 0.3785
4 nan 9 0.301
5 175 9 0.252
6 220 7 0.279
7 241 6 0.297
8 278 6 0.2235
9 213 5 0.065
10 281 4 0.4115

Entire Cohort

In [14]:
h = get_groupby(master_hotspot,'tp53_vc_group_1', 'count').sort_values(by='count', ascending=False)
display(h)

h = h.T
h = h[mutation_list]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax, colormap="Accent")
ax.legend(['In Frame', 'Truncated', 'Missense', 'Hotspot 248','Hotspot 273','Hotspot 175', 'Other Hotspot'],loc='center left', bbox_to_anchor=(1.1, 0.5), fontsize=11)
ax.set_title('Mutation Type - {} - No WGD'.format(cancer), weight='bold', fontsize=18)

plt.show()
count
tp53_vc_group_1
missense 77
truncated 38
hotspot 22
273 20
248 16
175 9
in_frame 6
In [15]:
fig, ax = boxplot_sampletype(df=master_hotspot,
                  group='tp53_vc_group_1',
                  palette=mutation_palette,
                  order=mutation_list,
                  metrics='frac_genome_altered',
                  figsize=(6,10),
                  title='Fraction of Genome Altered - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

TP53 Residual Subgroups

In [16]:
print('Number of Bi Allelic samples (with 1 mut): ' + str(master_hotspot[master_hotspot['tp53_res_group'] == 'no_tp53_res'].shape[0]))
print('')
print('Number of  TP53 Residual samples (with 1 mut): ' + str(master_hotspot[master_hotspot['tp53_res_group'] == 'tp53_res'].shape[0]))

Number of Bi Allelic samples (with 1 mut): 147

Number of  TP53 Residual samples (with 1 mut): 35
In [17]:
total_df = []
for group in ['no_tp53_res']:
    h = get_groupby(master_hotspot[master_hotspot['tp53_res_group'] == group], 'tp53_vc_group_1', group).sort_values(by=group, ascending=False)
    total_df.append(h)
    
    h = h.T
    h = h[mutation_list]
    fig = plt.figure(figsize=(6,1))
    ax = plt.subplot()

    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)

    h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax, colormap="Accent")
    if group == 'tp53_res':
        ax.legend(['In Frame', 'Truncated', 'Missense', 'Hotspot 248','Hotspot 273','Hotspot 175', 'Other Hotspot'],loc='center left', bbox_to_anchor=(1.05, 0.5), fontsize=11)
    else: ax.get_legend().remove()
    ax.set_title('Mutation Type - {} - No WGD'.format(group), weight='bold', fontsize=18)

    plt.show()

display_side_by_side(total_df[0])
no_tp53_res
tp53_vc_group_1
missense 60
truncated 29
273 18
hotspot 16
248 11
175 8
in_frame 5
In [18]:
for group in ['no_tp53_res']:
    master_wt = master_hotspot[master_hotspot['tp53_res_group'] == group]

    fig, ax = boxplot_sampletype(df=master_wt,
                      group='tp53_vc_group_1',
                      palette=mutation_palette,
                      order=mutation_list,
                      metrics='frac_genome_altered',
                      figsize=(6,10),
                      title='Fraction of Genome Altered - No WGD - {} subgroup'.format(group),
                      xlim=[0,1])
    plt.show()

SNV / INDEL Analysis

In this section we compare SNV and INDEL mutations. As in the previous section, we cut the cohort to keep only samples with exactly 1 tp53 mutation.

# of Drivers / SNV Drivers / INDEL Drivers

In [19]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='driver_mutation_count',
                  figsize=(8,12),
                  title='Driver Mutation Count - TP53 Subroups - No WGD',
                  xlim=[-0.1,50])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size driver_mutation_count
1 1_WILD_TYPE 35 7
2 0_HETLOSS 12 2
3 Statistics p-value
4 2.40315 0.0162543
0 1 2
0 size driver_mutation_count
1 1_WILD_TYPE 35 7
2 >=1_LOSS 125 2
3 Statistics p-value
4 4.8003 1.58428e-06
0 1 2
0 size driver_mutation_count
1 >1muts 22 18.5
2 >=1_LOSS 125 2
3 Statistics p-value
4 5.37291 7.74779e-08
In [20]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='snv_driver_mutation_count',
                  figsize=(8,12),
                  title='SNV Driver Mutation Count - TP53 Subroups - No WGD',
                  xlim=[-0.1,15])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='snv_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='snv_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='snv_driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size snv_driver_mutation_count
1 1_WILD_TYPE 35 4
2 0_HETLOSS 12 2
3 Statistics p-value
4 2.19578 0.028108
0 1 2
0 size snv_driver_mutation_count
1 1_WILD_TYPE 35 4
2 >=1_LOSS 125 1
3 Statistics p-value
4 4.54027 5.61832e-06
0 1 2
0 size snv_driver_mutation_count
1 >1muts 22 6
2 >=1_LOSS 125 1
3 Statistics p-value
4 5.46521 4.62348e-08
In [21]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='indel_driver_mutation_count',
                  figsize=(8,12),
                  title='INDEL Driver Mutation Count - TP53 Subroups - No WGD',
                  xlim=[-0.1,35])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 35 3
2 0_HETLOSS 12 0.5
3 Statistics p-value
4 2.48855 0.0128267
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 35 3
2 >=1_LOSS 125 0
3 Statistics p-value
4 4.9427 7.70483e-07
0 1 2
0 size indel_driver_mutation_count
1 >1muts 22 4
2 >=1_LOSS 125 0
3 Statistics p-value
4 3.64348 0.000268981

Genome Instability

The idea here is to see if we have differences in Fraction of Genome Altered if we cut our Cancer cohort on the number of drivers per sample.

Do we have more instability with more INDEL Driver Mutations within the same subgroup?

1_WILD_TYPE Subgroup

In [22]:
master_no_wgd_cancer_wt = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '1_WILD_TYPE']

thr=6

def get_driver_groups(x):
    if x.driver_mutation_count > thr:
        return 'High Co-Driver Count'
    if x.driver_mutation_count <= thr:
        return 'Low Co-Driver Count'
    

master_no_wgd_cancer_wt['co_driver_group'] = master_no_wgd_cancer_wt.apply(get_driver_groups, axis=1)
In [23]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_wt,
                  group='co_driver_group',
                  palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
                  order=['High Co-Driver Count', 'Low Co-Driver Count'],
                  metrics='frac_genome_altered',
                  figsize=(4,10),
                  title='Fraction of Genome Altered - 1_WILD_TYPE subgroup - Co Driver Count (thr={}) - {}'.format(thr,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_no_wgd_cancer_wt,
               group='co_driver_group',
               metrics='frac_genome_altered',
               group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[23]:
0 1 2
0 size frac_genome_altered
1 High Co-Driver Count 19 0.054
2 Low Co-Driver Count 16 0.1355
3 Statistics p-value
4 -0.711936 0.476504
In [24]:
def plot_density(df: pd.DataFrame, xlabel='', ylabel='', title='',clip = (0,3), group = None,  figsize=(5,5)):
    sns.set_style("whitegrid", {'grid.color': '1.'})
    fig, ax = plt.subplots(figsize=figsize)
    
    
    data =df['tp53_ccf_1']
    ax = sns.distplot(data,kde_kws={'clip': clip, "shade": True}, hist=False)
    ax.set_ylabel(ylabel)
    ax.set_xlabel(xlabel)
    ax.set_title('TP53 CCF for 1_WT in Non WGD Samples'  + title + ' (' + str(len(data)) + ')', weight = 'bold')
    
    mean=round(data.mean(),2) ; median=round(data.median(),2)
    string = 'Mean: '+ str(mean) +'\nMedian: ' + str(median)
    ax.axvline(mean, color='g', linestyle='-', label='Mean: '+ str(mean))
    ax.axvline(median, color='b', linestyle='-', label='Median: ' + str(median))
    ax.legend()
    #ax.set_xlim([0,1])
    
    plt.show()
In [25]:
master_high_count = master_no_wgd_cancer_wt[master_no_wgd_cancer_wt['co_driver_group'] == 'High Co-Driver Count']
master_low_count = master_no_wgd_cancer_wt[master_no_wgd_cancer_wt['co_driver_group'] == 'Low Co-Driver Count']

plot_density(df=master_high_count,
             xlabel='TP53 CCF', 
             ylabel='density estimation',
             title=' - High Co-Driver Count',
             clip = (0,1), 
             group = None,  figsize=(7,3))
plt.show()

plot_density(df=master_low_count,
             xlabel='TP53 CCF', 
             ylabel='density estimation',
             title=' - Low Co-Driver Count',
             clip = (0,1), 
             group = None,  figsize=(7,3))

plt.show()

So we see that samples with less co-drivers have a higher Genome Instability

0_HETLOSS

In [26]:
master_no_wgd_cancer_het = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '0_HETLOSS']

thr=0

def get_driver_groups(x):
    if x.indel_driver_mutation_count > thr:
        return 'High Co-Driver Count'
    if x.indel_driver_mutation_count <= thr:
        return 'Low Co-Driver Count'
    

master_no_wgd_cancer_het['co_driver_group'] = master_no_wgd_cancer_het.apply(get_driver_groups, axis=1)
In [27]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_het,
                  group='co_driver_group',
                  palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
                  order=['High Co-Driver Count', 'Low Co-Driver Count'],
                  metrics='frac_genome_altered',
                  figsize=(4,10),
                  title='Fraction of Genome Altered - 0_HETLOSS subgroup - INDEL Co Driver Count (thr={}) - {}'.format(thr,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_no_wgd_cancer_het,
               group='co_driver_group',
               metrics='frac_genome_altered',
               group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[27]:
0 1 2
0 size frac_genome_altered
1 High Co-Driver Count 6 0.2515
2 Low Co-Driver Count 6 0.2665
3 Statistics p-value
4 -0.160128 0.87278

Low CCF Analysis

In [28]:
thr_ccf_1 = 0.9

def ccf_subgroup(x):
    if x.tp53_ccf_1 <= thr_ccf_1: return 'low'
    elif x.tp53_ccf_1 > thr_ccf_1: return 'high'

master_no_wgd_cancer['ccf_group'] = master_no_wgd_cancer.apply(ccf_subgroup, axis=1)
get_groupby(master_no_wgd_cancer, 'ccf_group', 'count')
Out[28]:
count
ccf_group
high 153
low 56
In [29]:
pd.DataFrame(master_no_wgd_cancer[['ccf_group', 'tp53_group']].groupby([ 'tp53_group', 'ccf_group']).size())
Out[29]:
0
tp53_group ccf_group
1_WILD_TYPE high 19
low 15
>1muts high 9
low 12
>=1_LOSS high 105
low 20
>=1_cnLOH high 16
low 6
HOMDEL low 1
In [30]:
master_no_wgd_cancer_low = master_no_wgd_cancer[(master_no_wgd_cancer['ccf_group'] == 'low') | (master_no_wgd_cancer['tp53_count'] == 0)]
get_groupby(master_no_wgd_cancer_low, 'tp53_group', 'count')
Out[30]:
count
tp53_group
0_HETLOSS 12
1_WILD_TYPE 15
>1muts 12
>=1_LOSS 20
>=1_cnLOH 6
HOMDEL 2

# of Drivers

In [31]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_low,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='indel_driver_mutation_count',
                  figsize=(8,12),
                  title='INDEL Driver Mutation Count - TP53 Subroups - No WGD - Low TP53 CCF',
                  xlim=[-0.1,30])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer_low,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer_low,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer_low,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 15 8
2 0_HETLOSS 12 0.5
3 Statistics p-value
4 2.46415 0.013734
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 15 8
2 >=1_LOSS 20 0
3 Statistics p-value
4 3.11667 0.00182908
0 1 2
0 size indel_driver_mutation_count
1 >1muts 12 5.5
2 >=1_LOSS 20 0
3 Statistics p-value
4 2.47173 0.0134459

GI in 1_WILD_TYPE

In [32]:
master_no_wgd_cancer_low_wt = master_no_wgd_cancer_low[master_no_wgd_cancer_low['tp53_group'] == '1_WILD_TYPE']

thr=6

def get_driver_groups(x):
    if x.driver_mutation_count > thr:
        return 'High Co-Driver Count'
    if x.driver_mutation_count <= thr:
        return 'Low Co-Driver Count'
    

master_no_wgd_cancer_low_wt['co_driver_group'] = master_no_wgd_cancer_low_wt.apply(get_driver_groups, axis=1)

fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_low_wt,
                  group='co_driver_group',
                  palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
                  order=['High Co-Driver Count', 'Low Co-Driver Count'],
                  metrics='frac_genome_altered',
                  figsize=(4,10),
                  title='Fraction of Genome Altered - 1_WILD_TYPE subgroup - Low TP53 CCF - Co Driver Count (thr={}) - {}'.format(thr,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_no_wgd_cancer_low_wt,
               group='co_driver_group',
               metrics='frac_genome_altered',
               group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[32]:
0 1 2
0 size frac_genome_altered
1 High Co-Driver Count 9 0.048
2 Low Co-Driver Count 6 0.029
3 Statistics p-value
4 1.94454 0.0518299

High CCF Analysis

In [33]:
master_no_wgd_cancer_high = master_no_wgd_cancer[(master_no_wgd_cancer['ccf_group'] == 'high') | (master_no_wgd_cancer['tp53_count'] == 0)]
get_groupby(master_no_wgd_cancer_high, 'tp53_group', 'count')
Out[33]:
count
tp53_group
0_HETLOSS 12
1_WILD_TYPE 19
>1muts 9
>=1_LOSS 105
>=1_cnLOH 16
HOMDEL 1

# of Drivers

In [34]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_high,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='driver_mutation_count',
                  figsize=(8,12),
                  title='Driver Mutation Count - TP53 Subroups - No WGD - High TP53 CCF',
                  xlim=[-0.1,40])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer_high,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer_high,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer_high,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 19 2
2 0_HETLOSS 12 0.5
3 Statistics p-value
4 2.21027 0.0270866
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 19 2
2 >=1_LOSS 105 0
3 Statistics p-value
4 3.77019 0.000163121
0 1 2
0 size indel_driver_mutation_count
1 >1muts 9 1
2 >=1_LOSS 105 0
3 Statistics p-value
4 2.02807 0.042553

GI in 1_WILD_TYPE

In [35]:
master_no_wgd_cancer_high_wt = master_no_wgd_cancer_high[master_no_wgd_cancer_high['tp53_group'] == '1_WILD_TYPE']

thr=6

def get_driver_groups(x):
    if x.driver_mutation_count > thr:
        return 'High Co-Driver Count'
    if x.driver_mutation_count <= thr:
        return 'Low Co-Driver Count'
    

master_no_wgd_cancer_high_wt['co_driver_group'] = master_no_wgd_cancer_high_wt.apply(get_driver_groups, axis=1)

fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_high_wt,
                  group='co_driver_group',
                  palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
                  order=['High Co-Driver Count', 'Low Co-Driver Count'],
                  metrics='frac_genome_altered',
                  figsize=(4,10),
                  title='Fraction of Genome Altered - 1_WILD_TYPE subgroup - High TP53 CCF - Co Driver Count (thr={}) - {}'.format(thr,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_no_wgd_cancer_high_wt,
               group='co_driver_group',
               metrics='frac_genome_altered',
               group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[35]:
0 1 2
0 size frac_genome_altered
1 High Co-Driver Count 10 0.0575
2 Low Co-Driver Count 9 0.207
3 Statistics p-value
4 -1.87794 0.0603891
In [36]:
get_hotspot_frac(df=master_no_wgd_cancer_high_wt[master_no_wgd_cancer_high_wt['co_driver_group'] == 'High Co-Driver Count'],
                group_type=None,
                group=None)
Out[36]:
0 1 2
0 spot # frac
1 11 1 0.35
2 110 1 0.366
3 137 1 0
4 139 1 0.046
5 152 1 0.253
6 196 1 0.054
7 213 1 0.026
8 220 1 0
9 283 1 0.145
10 73 1 0.061
In [37]:
get_hotspot_frac(df=master_no_wgd_cancer_high_wt[master_no_wgd_cancer_high_wt['co_driver_group'] == 'Low Co-Driver Count'],
                group_type=None,
                group=None)
Out[37]:
0 1 2
0 spot # frac
1 245 2 0.3485
2 248 2 0.248
3 196 1 0.213
4 249 1 0.122
5 266 1 0.353
6 270 1 0.195
7 47 1 0.149
In [38]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_high_wt,
                  group='co_driver_group',
                  palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
                  order=['High Co-Driver Count', 'Low Co-Driver Count'],
                  metrics='Patient_Current_Age',
                  figsize=(4,10),
                  title='Patient Age - 1_WILD_TYPE subgroup - High TP53 CCF - Co Driver Count (thr={}) - {}'.format(thr,cancer),
                  xlim=[20,100])
plt.show()
In [39]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis', fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(['High Co-Driver Count', 'Low Co-Driver Count'], range(len(['High Co-Driver Count', 'Low Co-Driver Count']))):
    data = master_no_wgd_cancer_high_wt[master_no_wgd_cancer_high_wt['co_driver_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
    kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
    kmf.plot_survival_function(color = ['#FF9900' ,'#146EB4'][i], ax=ax)
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-39-5ad86ffeb97b> in <module>
      2 ax = fig.add_subplot(111)
      3 fig.suptitle('Survival Analysis', fontsize=16, weight='bold')
----> 4 kmf = KaplanMeierFitter()
      5 
      6 for group,i in zip(['High Co-Driver Count', 'Low Co-Driver Count'], range(len(['High Co-Driver Count', 'Low Co-Driver Count']))):

NameError: name 'KaplanMeierFitter' is not defined

Co Driver Analysis

In [40]:
codrivers_cancer = get_major_codrivers(master=master_no_wgd_cancer,
                    maf=maf_cohort_nowgd,
                    head=15)

codrivers_cancer_tp53 = get_major_codrivers(master=master_no_wgd_cancer[master_no_wgd_cancer['tp53_count'] >= 1],
                    maf=maf_cohort_nowgd,
                    head=15)
In [41]:
co_drivers = pd.merge(codrivers_cancer, codrivers_cancer_tp53, on='Hugo_Symbol')
co_drivers.columns = ['cancer', 'cancer_tp53']
In [42]:
co_drivers['ratio'] = co_drivers.apply(lambda x: 100*round(x.cancer_tp53/x.cancer, 4) , axis=1)
co_drivers = co_drivers.sort_values(by='ratio', ascending=False)
In [43]:
co_drivers
Out[43]:
cancer cancer_tp53 ratio
Hugo_Symbol
FBXW7 81 45 55.56
PIK3CA 347 99 28.53
PIK3R1 238 49 20.59
KMT2B 99 20 20.20
INPPL1 118 23 19.49
JAK1 115 22 19.13
ZFHX3 121 23 19.01
KMT2D 136 25 18.38
CTCF 142 25 17.61
PTEN 555 90 16.22
ARID1A 460 70 15.22
KRAS 139 19 13.67
In [44]:
labels = []
for element in co_drivers.index.tolist():
    labels.append(element + ' ('+ str(int(co_drivers.loc[element]['cancer']))+')')

ax = sns.barplot(y=co_drivers.index, x='ratio',data=co_drivers[['ratio']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Co-Drivers Enrichment in TP53 State')
Out[44]:
Text(0.5, 1.0, 'Co-Drivers Enrichment in TP53 State')
In [45]:
codrivers_cancer
Out[45]:
count
Hugo_Symbol
PTEN 555
ARID1A 460
PIK3CA 347
PIK3R1 238
CTNNB1 166
CTCF 142
KRAS 139
KMT2D 136
ZFHX3 121
INPPL1 118
JAK1 115
KMT2B 99
FBXW7 81
BCOR 78
RNF43 73
In [46]:
labels = []
codrivers_cancer = get_major_codrivers(master=master_no_wgd_cancer,
                                       maf=maf_cohort_nowgd,
                                       head=15,
                                      tp53=True)

codrivers_cancer['proportion'] = codrivers_cancer.apply(lambda x: 100* round(x['count'] / codrivers_cancer.sum().values[0], 4), axis=1)

for element in codrivers_cancer.head(10).index.tolist():
    labels.append(element + ' ('+ str(int(codrivers_cancer.loc[element]['count']))+')')

ax = sns.barplot(y=codrivers_cancer.head(10).index, x='proportion',data=codrivers_cancer.head(10)[['proportion']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Drivers Frequency in {}'.format(cancer))
Out[46]:
Text(0.5, 1.0, 'Drivers Frequency in Endometrial Cancer')

Co-Drivers per subgroup

In [47]:
co_drivers_res = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_res_group',
                                                group_1='tp53_res',
                                                group_2='no_tp53_res')
co_drivers_res
Out[47]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
PTEN 46 -8.57 44 7.48
ARID1A 42 -7.82 25 4.25
PIK3CA 28 -5.21 69 11.73
PIK3R1 20 -3.72 31 5.27
ZFHX3 18 -3.35 4 0.68
INPPL1 16 -2.98 6 1.02
CTCF 15 -2.79 8 1.36
KMT2B 15 -2.79 5 0.85
JAK1 15 -2.79 4 0.68
FBXW7 12 -2.23 34 5.78
NF1 9 -1.68 7 1.19
MSH2 9 -1.68 3 0.51
KMT2D 8 -1.49 14 2.38
KRAS 8 -1.49 13 2.21
RNF43 7 -1.30 4 0.68
APC 7 -1.30 4 0.68
FGFR2 7 -1.30 4 0.68
RASA1 7 -1.30 6 1.02
POLE 7 -1.30 5 0.85
NSD1 7 -1.30 2 0.34
FAT1 7 -1.30 5 0.85
ARID1B 7 -1.30 6 1.02
CTNNB1 6 -1.12 3 0.51
ATM 6 -1.12 7 1.19
EP300 6 -1.12 7 1.19
RB1 6 -1.12 10 1.70
CREBBP 6 -1.12 5 0.85
ARID5B 5 -0.93 2 0.34
MSH3 5 -0.93 4 0.68
PPP2R1A 5 -0.93 32 5.44
MSH6 5 -0.93 5 0.85
DICER1 4 -0.74 5 0.85
CIC 4 -0.74 3 0.51
BRCA2 4 -0.74 3 0.51
TERT 4 -0.74 4 0.68
BCOR 4 -0.74 3 0.51
CASP8 4 -0.74 3 0.51
MGA 3 -0.56 2 0.34
ATRX 3 -0.56 4 0.68
KDM5C 3 -0.56 4 0.68
KMT2C 3 -0.56 4 0.68
NRAS 3 -0.56 2 0.34
NFE2L2 3 -0.56 5 0.85
NOTCH1 3 -0.56 4 0.68
TET2 3 -0.56 4 0.68
PAX5 2 -0.37 4 0.68
PTPRT 2 -0.37 2 0.34
NCOR1 2 -0.37 3 0.51
MAP3K1 2 -0.37 6 1.02
MED12 2 -0.37 8 1.36
KMT2A 2 -0.37 7 1.19
FLT3 2 -0.37 2 0.34
BLM 2 -0.37 5 0.85
STAG2 2 -0.37 2 0.34
SPEN 2 -0.37 4 0.68
ERRFI1 2 -0.37 2 0.34
SOX17 2 -0.37 2 0.34
KDM6A 2 -0.37 2 0.34
ATR 2 -0.37 2 0.34
SETD2 2 -0.37 4 0.68
SH2B3 2 -0.37 1 0.17
TSC2 1 -0.19 4 0.68
SPOP 1 -0.19 10 1.70
SOS1 1 -0.19 3 0.51
In [48]:
fig=plt.figure(figsize=(7,7))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_res[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = ['#2ECC71','#1E8449'])
ax.legend(['TP53 Residual', 'No TP53 Residual'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)

plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [25, 20, 15, 10, 5, 0, 5, 10, 15, 20, 25]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)

plt.show()
In [49]:
co_drivers_cnloh_loss = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='>=1_cnLOH',
                                                group_2='>=1_LOSS')
co_drivers_cnloh_loss
Out[49]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
PIK3CA 11 -17.74 50 17.36
PTEN 8 -12.90 21 7.29
PPP2R1A 5 -8.06 22 7.64
PIK3R1 5 -8.06 21 7.29
ARID1A 4 -6.45 10 3.47
CDKN2A 4 -6.45 1 0.35
FBXW7 2 -3.23 28 9.72
NFE2L2 1 -1.61 2 0.69
U2AF1 1 -1.61 1 0.35
TSC2 1 -1.61 2 0.69
SPOP 1 -1.61 8 2.78
SOS1 1 -1.61 1 0.35
RB1 1 -1.61 4 1.39
NCOR1 1 -1.61 1 0.35
ARID1B 1 -1.61 4 1.39
KRAS 1 -1.61 9 3.12
KMT2D 1 -1.61 5 1.74
KMT2C 1 -1.61 2 0.69
KDM6A 1 -1.61 1 0.35
FGFR2 1 -1.61 2 0.69
FAT1 1 -1.61 2 0.69
EP300 1 -1.61 2 0.69
ZFHX3 1 -1.61 3 1.04
In [50]:
fig=plt.figure(figsize=(7,7))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_cnloh_loss[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[4],mc[0]])
ax.legend(['>=1_cnLOH', '>=1_LOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)

plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [20, 15, 10, 5, 0, 5, 10, 15, 20, 25]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)

plt.show()

Proportion are the same in both groups

In [51]:
co_drivers_losses = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='0_HETLOSS',
                                                group_2='>=1_LOSS')
co_drivers_losses
Out[51]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
PIK3R1 4 -11.76 21 7.29
FBXW7 3 -8.82 28 9.72
TERT 2 -5.88 3 1.04
PTEN 2 -5.88 21 7.29
PIK3CA 2 -5.88 50 17.36
KRAS 2 -5.88 9 3.12
ARID1A 2 -5.88 10 3.47
NF1 1 -2.94 4 1.39
SOS1 1 -2.94 1 0.35
APC 1 -2.94 3 1.04
KMT2A 1 -2.94 1 0.35
EP300 1 -2.94 2 0.69
CDKN2A 1 -2.94 1 0.35
MED12 1 -2.94 5 1.74
In [52]:
fig=plt.figure(figsize=(7,7))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_losses[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[5],mc[0]])
ax.legend(['0_HETLOSS', '>=1_LOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)

plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
#a = [-40, -30, -20, -10, 0, 10, 20, 30, 40]
#ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)

plt.show()

Same proportions, enrichment in APC and KRAS

In [53]:
co_drivers_mult_cnloh = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='>1muts',
                                                group_2='>=1_cnLOH')
co_drivers_mult_cnloh
Out[53]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
PTEN 27 -6.37 8 12.90
ARID1A 23 -5.42 4 6.45
PIK3CA 11 -2.59 11 17.74
PIK3R1 9 -2.12 5 8.06
KMT2D 9 -2.12 1 1.61
RB1 9 -2.12 1 1.61
FBXW7 7 -1.65 2 3.23
ZFHX3 7 -1.65 1 1.61
PPP2R1A 6 -1.42 5 8.06
EP300 6 -1.42 1 1.61
PAX5 4 -0.94 1 1.61
FAT1 4 -0.94 1 1.61
FGFR2 4 -0.94 1 1.61
KRAS 4 -0.94 1 1.61
KMT2C 3 -0.71 1 1.61
NFE2L2 2 -0.47 1 1.61
NCOR1 2 -0.47 1 1.61
ARID1B 2 -0.47 1 1.61
CDKN2A 1 -0.24 4 6.45
In [54]:
get_major_codrivers(master=master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>1muts'],
                    maf=maf_cohort_nowgd,
                    head=100)
Out[54]:
count
Hugo_Symbol
PTEN 27
ARID1A 23
CTCF 12
JAK1 11
PIK3CA 11
KMT2B 11
ATM 11
INPPL1 10
PIK3R1 9
KMT2D 9
RB1 9
POLE 8
CREBBP 8
NF1 8
FBXW7 7
MSH6 7
ZFHX3 7
RASA1 6
PPP2R1A 6
KMT2A 6
TET2 6
EP300 6
APC 6
MSH2 6
EGFR 5
RNF43 5
CASP8 5
NSD1 5
BCOR 5
KDM5C 5
ATRX 5
MAP3K1 5
PAX5 4
FAT1 4
FGFR2 4
NOTCH1 4
DICER1 4
PTPRD 4
MSH3 4
SETD2 4
BRCA2 4
SMAD2 4
SMARCA4 4
KRAS 4
IFNGR1 3
FLT3 3
MED12 3
PIK3R3 3
KMT2C 3
ERRFI1 3
TET1 3
ATR 3
SPEN 3
CIC 3
BLM 3
NFE2L2 2
ANKRD11 2
TGFBR1 2
TERT 2
LATS1 2
SMO 2
SH2B3 2
SESN3 2
NCOR1 2
MLH1 2
RUNX1 2
PMS1 2
MTOR 2
PBRM1 2
NBN 2
NRAS 2
NOTCH2 2
AMER1 2
MGA 2
ARID1B 2
DIS3 2
INHBA 2
BRAF 2
HNF1A 2
CTLA4 2
ARID5B 2
ASXL1 2
ASXL2 2
KIT 1
SDHD 1
SMAD3 1
BRCA1 1
SF3B1 1
BTK 1
CBL 1
RTEL1 1
CDK12 1
BCL2L11 1
CDKN2A 1
RECQL4 1
CHEK1 1
RAD51D 1
RAD51B 1
SMAD4 1
SOX17 1
In [55]:
fig=plt.figure(figsize=(10,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_mult_cnloh[['proportion_1', 'proportion_2']].head(20)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[3],mc[4]])
ax.legend(['>1muts', '>=1_cnLOH'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=15)

plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [-20, -10, 0, 10, 20, 30, 40]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)

plt.show()
In [56]:
co_drivers_wt_loss = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='1_WILD_TYPE',
                                                group_2='>=1_LOSS')
co_drivers_wt_loss
Out[56]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
PTEN 32 -8.65 21 7.29
ARID1A 29 -7.84 10 3.47
PIK3CA 23 -6.22 50 17.36
PIK3R1 12 -3.24 21 7.29
ZFHX3 11 -2.97 3 1.04
INPPL1 11 -2.97 1 0.35
CTCF 9 -2.43 2 0.69
JAK1 8 -2.16 1 0.35
FBXW7 8 -2.16 28 9.72
ARID1B 7 -1.89 4 1.39
KMT2D 7 -1.89 5 1.74
RNF43 6 -1.62 1 0.35
MSH2 5 -1.35 1 0.35
FAT1 5 -1.35 2 0.69
KRAS 5 -1.35 9 3.12
NF1 5 -1.35 4 1.39
RASA1 5 -1.35 4 1.39
FGFR2 4 -1.08 2 0.69
ERBB2 4 -1.08 1 0.35
APC 4 -1.08 3 1.04
DICER1 4 -1.08 1 0.35
PPP2R1A 4 -1.08 22 7.64
RB1 3 -0.81 4 1.39
NFE2L2 3 -0.81 2 0.69
ANKRD11 3 -0.81 1 0.35
EP300 3 -0.81 2 0.69
CASP8 2 -0.54 2 0.69
CREBBP 2 -0.54 1 0.35
CHEK2 2 -0.54 1 0.35
NOTCH1 2 -0.54 1 0.35
NOTCH3 2 -0.54 1 0.35
BRCA2 2 -0.54 1 0.35
NRAS 2 -0.54 1 0.35
BLM 2 -0.54 2 0.69
BCOR 2 -0.54 1 0.35
SOX17 2 -0.54 1 0.35
SPEN 2 -0.54 1 0.35
STAG2 2 -0.54 1 0.35
SMAD2 1 -0.27 2 0.69
PTPRT 1 -0.27 2 0.69
ATM 1 -0.27 1 0.35
TSC2 1 -0.27 2 0.69
TERT 1 -0.27 3 1.04
B2M 1 -0.27 1 0.35
RIT1 1 -0.27 1 0.35
SPOP 1 -0.27 8 2.78
ERRFI1 1 -0.27 1 0.35
ERBB3 1 -0.27 1 0.35
KDM5C 1 -0.27 1 0.35
KDM6A 1 -0.27 1 0.35
KEAP1 1 -0.27 2 0.69
In [57]:
fig=plt.figure(figsize=(10,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_wt_loss[['proportion_1', 'proportion_2']].head(20)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[2],mc[0]])
ax.legend(['1_WILD_TYPE', '>=1_LOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=15)

plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
#a = [-10, 0, 10, 20, 30, 40]
#ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)

plt.show()

Comparison with WGD Cohort (WGD - TP53 - LOH)

In [58]:
def get_major_codrivers(master: pd.DataFrame, maf: pd.DataFrame, head:int = 10):
    samples = master.Tumor_Id.tolist()
    maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True][maf['Hugo_Symbol'] != 'TP53']
    h = pd.DataFrame(maf_filtered[['Hugo_Symbol']].groupby(['Hugo_Symbol']).size())
    h.columns = ['count']
    h = h.sort_values(by='count', ascending=False).head(head)
    
    return(h)

def create_co_drivers_table_wgd(master_1: pd.DataFrame, master_2: pd.DataFrame, group_type:str,  group_1: str):
    master_group_1 = master_1[master_1[group_type] == group_1]
    co_drivers_group_1 = get_major_codrivers(master=master_group_1,
                        maf=maf_cohort_nowgd,
                       head=100)
    co_drivers_group_1['proportion_1'] = co_drivers_group_1.apply(lambda x: 100* round(x['count'] / co_drivers_group_1.sum().values[0], 4), axis=1)
    
    master_group_2 = master_2[master_2['tp53_count'] >=1][master_2['tp53_loh_status'] == True]
    co_drivers_group_2 = get_major_codrivers(master=master_group_2,
                        maf=maf_cohort_wgd,
                       head=100)
    co_drivers_group_2['proportion_2'] = co_drivers_group_2.apply(lambda x: 100* round(x['count'] / co_drivers_group_2.sum().values[0], 4), axis=1)
    
    co_drivers_groups = pd.merge(co_drivers_group_2, co_drivers_group_1, on='Hugo_Symbol')
    co_drivers_groups['proportion_2'] = - co_drivers_groups['proportion_2']
    
    return co_drivers_groups
In [59]:
co_drivers_wgd_loss = create_co_drivers_table_wgd(master_1=master_no_wgd_cancer,
                            master_2=master_wgd_cancer,
                            group_type='tp53_group',
                            group_1='>=1_LOSS')
co_drivers_wgd_loss
Out[59]:
count_x proportion_2 count_y proportion_1
Hugo_Symbol
PIK3CA 50 -18.12 50 17.36
PPP2R1A 39 -14.13 22 7.64
PIK3R1 27 -9.78 21 7.29
PTEN 20 -7.25 21 7.29
FBXW7 17 -6.16 28 9.72
KRAS 11 -3.99 9 3.12
ARID1A 7 -2.54 10 3.47
SPOP 6 -2.17 8 2.78
RB1 5 -1.81 4 1.39
NRAS 5 -1.81 1 0.35
KMT2C 5 -1.81 2 0.69
MED12 4 -1.45 5 1.74
ERBB3 3 -1.09 1 0.35
MAP3K1 3 -1.09 1 0.35
AKT1 3 -1.09 2 0.69
CASP8 3 -1.09 2 0.69
SOX17 2 -0.72 1 0.35
FAT1 2 -0.72 2 0.69
NOTCH1 2 -0.72 1 0.35
CREBBP 2 -0.72 1 0.35
FGFR2 2 -0.72 2 0.69
ERBB2 2 -0.72 1 0.35
U2AF1 2 -0.72 1 0.35
LATS1 2 -0.72 2 0.69
BRAF 1 -0.36 4 1.39
EP300 1 -0.36 2 0.69
RIT1 1 -0.36 1 0.35
STAG2 1 -0.36 1 0.35
STK11 1 -0.36 1 0.35
TERT 1 -0.36 3 1.04
TSC2 1 -0.36 2 0.69
KEAP1 1 -0.36 2 0.69
ERRFI1 1 -0.36 1 0.35
KDM6A 1 -0.36 1 0.35
BRCA2 1 -0.36 1 0.35
KMT2D 1 -0.36 5 1.74
APC 1 -0.36 3 1.04
ZFHX3 1 -0.36 3 1.04
In [60]:
fig=plt.figure(figsize=(8,8))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_wgd_loss[['proportion_2', 'proportion_1']].head(15)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = ['#7F8C8D',mc[0]])
ax.legend(['WGD - TP53 - LOH', '>=1_LOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)

plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
#a = [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
#ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)

plt.show()

Genome Instability Associated

In [61]:
def get_master_codrivers(master: pd.DataFrame, maf: pd.DataFrame, symbol: str):
    samples = master.Tumor_Id.tolist()
    samples_final = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['Hugo_Symbol'] == symbol].Tumor_Sample_Barcode.tolist()

    master_filtered = master[master.Tumor_Id.isin(samples_final)]
    
    return master_filtered

>=1_cnLOH

In [62]:
master_no_wgd_cancer_cnloh = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>=1_cnLOH']
master_PIK3CA = get_master_codrivers(master=master_no_wgd_cancer_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='PIK3CA')

master_RB1 = get_master_codrivers(master=master_no_wgd_cancer_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='RB1')

master_no_wgd_cancer_cnloh['data'] = '>=1_cnLOH'
master_RB1['data'] = 'RB1'
master_PIK3CA['data'] = 'PIK3CA'

masters = [master_no_wgd_cancer_cnloh, master_RB1, master_PIK3CA]
allMasters = pd.concat(masters)
In [63]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >=1_cnLOH')
ax.set_xlabel('')
Out[63]:
Text(0.5, 0, '')

>=1_LOSS

In [64]:
master_no_wgd_cancer_loss = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>=1_LOSS']
master_CDH1 = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='CDH1')

master_GATA3 = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='GATA3')

master_PIK3CA = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='PIK3CA')

master_no_wgd_cancer_loss['data'] = '>=1_loss'
master_CDH1['data'] = 'CDH1'
master_GATA3['data'] = 'GATA3'
master_PIK3CA['data'] = 'PIK3CA'

masters = [master_no_wgd_cancer_loss, master_CDH1, master_GATA3, master_PIK3CA]
allMasters = pd.concat(masters)
In [65]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >=1_LOSS')
ax.set_xlabel('')
Out[65]:
Text(0.5, 0, '')

0_HETLOSS

In [66]:
master_no_wgd_cancer_loss = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '0_HETLOSS']
master_ = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='APC')

master_CDH1 = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='CDH1')

master_GATA3 = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='GATA3')

master_PIK3CA = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='PIK3CA')

master_no_wgd_cancer_loss['data'] = '>=1_loss'
master_CDH1['data'] = 'CDH1'
master_GATA3['data'] = 'GATA3'
master_PIK3CA['data'] = 'PIK3CA'

masters = [master_no_wgd_cancer_loss, master_CDH1, master_GATA3, master_PIK3CA]
allMasters = pd.concat(masters)
In [67]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - 0_HETLOSS')
ax.set_xlabel('')
Out[67]:
Text(0.5, 0, '')

1_WILD_TYPE

In [68]:
master_no_wgd_cancer_wt = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '1_WILD_TYPE']

master_PIK3CA = get_master_codrivers(master=master_no_wgd_cancer_wt,
                                   maf=maf_cohort_nowgd,
                                   symbol='PIK3CA')

master_MAP3K1 = get_master_codrivers(master=master_no_wgd_cancer_wt,
                                   maf=maf_cohort_nowgd,
                                   symbol='MAP3K1')


master_no_wgd_cancer_wt['data'] = '1_WT'
master_PIK3CA['data'] = 'PIK3CA'
master_MAP3K1['data'] = 'MAP3K1'

masters = [master_no_wgd_cancer_wt, master_PIK3CA, master_MAP3K1]
allMasters = pd.concat(masters)
In [69]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - 1_WT')
ax.set_xlabel('')
Out[69]:
Text(0.5, 0, '')

CCF / VAF Analysis

Same here we take only samples with exactly 1 tp53 mutation (master_hotspot).

We have to define groups for CCF to see if there are differences between those groups. To have an idea of the CCF distribution we show here the distribution coming from the cancer_panel.

We see that our tp53_ccf distribution is very high for all subgroups. >=1_LOSS and 0_HETLOSS are the biggest subgroups - by far - and >=1_LOSS* has a very high CCF median.

It will be hard to cut the cohort based on the CCF. Let's try and see the size of the subcohorts:

In [70]:
master_ccf = master_no_wgd_cancer[(master_no_wgd_cancer['tp53_count'] == 1) | (master_no_wgd_cancer['tp53_group'] == '0_HETLOSS')]
In [71]:
thr_ccf_1 = 0.9 ; thr_ccf_2 = 0.95

def ccf_subgroup(x):
    if x.tp53_ccf_1 <= thr_ccf_1: return 'low'
    elif x.tp53_ccf_1 <= thr_ccf_2: return 'medium'
    elif x.tp53_ccf_1 > thr_ccf_2: return 'high'

master_ccf['ccf_group'] = master_ccf.apply(ccf_subgroup, axis=1)
get_groupby(master_ccf, 'ccf_group', 'count')
Out[71]:
count
ccf_group
high 118
low 43
medium 26
In [72]:
thr_vaf_1 = 0.3 ; thr_vaf_2 = 0.4

def vaf_subgroup(x):
    if x.tp53_vaf_1 <= thr_vaf_1: return 'low'
    elif x.tp53_vaf_1 <= thr_vaf_2: return 'medium'
    elif x.tp53_vaf_1 > thr_vaf_2: return 'high'

master_ccf['vaf_group'] = master_ccf.apply(vaf_subgroup, axis=1)       
get_groupby(master_ccf, 'vaf_group', 'count')
Out[72]:
count
vaf_group
high 73
low 85
medium 30

VAF Analysis

No VAF Cut

In [73]:
fig, ax = boxplot_sampletype(df=master_ccf,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_ccf,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['0_HETLOSS', '1_WILD_TYPE'])
Out[73]:
0 1 2
0 size frac_genome_altered
1 0_HETLOSS 12 0.2515
2 1_WILD_TYPE 35 0.065
3 Statistics p-value
4 2.85451 0.00431036

Low VAF

In [74]:
master_low = master_ccf[(master_ccf['vaf_group'] == 'low') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_low,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - VAF < {} - {}'.format(thr_vaf_1,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_low,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '>=1_LOSS'])
Out[74]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 28 0.103
2 >=1_LOSS 47 0.298
3 Statistics p-value
4 -4.73194 2.22379e-06

Medium VAF

In [75]:
master_med = master_ccf[(master_ccf['vaf_group'] == 'medium') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_med,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {} < VAF < {} - {}'.format(thr_vaf_1,thr_vaf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_med,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[75]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 5 0.047
2 0_HETLOSS 12 0.2515
3 Statistics p-value
4 -3.16228 0.0015654

High VAF

In [76]:
master_high = master_ccf[(master_ccf['vaf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_high,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - VAF > {} - {}'.format(thr_vaf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_high,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[76]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 2 0.2785
2 0_HETLOSS 12 0.2515
3 Statistics p-value
4 0.547723 0.583882

CCF Analysis

No CCF Cut

In [77]:
fig, ax = boxplot_sampletype(df=master_ccf,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_ccf,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['0_HETLOSS', '1_WILD_TYPE'])
Out[77]:
0 1 2
0 size frac_genome_altered
1 0_HETLOSS 12 0.2515
2 1_WILD_TYPE 35 0.065
3 Statistics p-value
4 2.85451 0.00431036

Low CCF

In [78]:
master_low = master_ccf[(master_ccf['ccf_group'] == 'low') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_low,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - CCF < {} - {}'.format(thr_ccf_1,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_low,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[78]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 15 0.046
2 0_HETLOSS 12 0.2515
3 Statistics p-value
4 -3.80601 0.000141227

Medium CCF

In [79]:
master_med = master_ccf[(master_ccf['ccf_group'] == 'medium') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_med,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {} < CCF < {} - {}'.format(thr_ccf_1,thr_ccf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_med,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[79]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 3 0.145
2 0_HETLOSS 12 0.2515
3 Statistics p-value
4 -0.866025 0.386476

High CCF

In [ ]:
master_high = master_ccf[(master_ccf['ccf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_high,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - CCF > {} - {}'.format(thr_ccf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_high,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])

Splitting on the different level of CCF / VAF

In [ ]:
fig, ax = boxplot_sampletype(df=master_hotspot,
                  group='vaf_group',
                  palette={'low': tab10[0] , 'medium': tab10[1], 'high':tab10[2]},
                  order=['low', 'medium', 'high'],
                  metrics='frac_genome_altered',
                  figsize=(3,10),
                  title='Fraction of Genome Altered - VAF levels - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_hotspot,
               group='vaf_group',
               metrics='frac_genome_altered',
               group_list=['low', 'medium'])
In [ ]:
fig, ax = boxplot_sampletype(df=master_hotspot,
                  group='ccf_group',
                  palette={'low': tab10[0] , 'medium': tab10[1], 'high':tab10[2]},
                  order=['low', 'medium', 'high'],
                  metrics='frac_genome_altered',
                  figsize=(3,10),
                  title='Fraction of Genome Altered - CCF levels - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_hotspot,
               group='ccf_group',
               metrics='frac_genome_altered',
               group_list=['low', 'medium'])

Clinical Correlates

Age

In [78]:
#fig=plt.figure(figsize=(10,3))
ax = plt.subplot2grid(shape=(4,1), loc=(0,0), colspan=1)

sns.boxplot(x='Patient_Current_Age',data=master_no_wgd_cancer, ax=ax).set_title('Patient Age - {}'.format(cancer), weight='bold', fontsize=14)


style(ax)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

TP53 Residual Groups

In [79]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_res_group',
                  palette=palette_res,
                  order=res_group_list,
                  metrics='Patient_Current_Age',
                  figsize=(3,10),
                  title='Patient Current Age - {}'.format(cancer),
                  xlim=[20,100])
plt.show()

get_statistics(df=master_no_wgd_cancer,
               group='tp53_res_group',
               metrics='Patient_Current_Age',
               group_list=['tp53_res', 'no_tp53_res'])
Out[79]:
0 1 2
0 size Patient_Current_Age
1 tp53_res 808 58
2 no_tp53_res 561 54.5
3 Statistics p-value
4 4.33976 1.42636e-05

TP53 Subgroups

In [80]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='Patient_Current_Age',
                  figsize=(7,10),
                  title='Patient Current Age - {}'.format(cancer),
                  xlim=[20,100])
plt.show()

get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='Patient_Current_Age',
               group_list=['1_WILD_TYPE', '>=1_cnLOH'])
Out[80]:
0 1 2
0 size Patient_Current_Age
1 1_WILD_TYPE 23 55
2 >=1_cnLOH 64 55.5
3 Statistics p-value
4 0.341683 0.732589

Sex

In [81]:
h = get_groupby(master_no_wgd_cancer,'Sex', 'count').sort_values(by='count', ascending=False)
display(h)

h = h.T
h = h[['Male', 'Female']]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax)
ax.legend(['Male', 'Female'],loc='center left', bbox_to_anchor=(1.1, 0.5), fontsize=11)
ax.set_title('Sex Distribution - {} - No WGD'.format(cancer), weight='bold', fontsize=18)

plt.show()
count
Sex
Female 2307
Male 10

Survival Analysis

In [82]:
from lifelines import KaplanMeierFitter
from lifelines.statistics import logrank_test
In [83]:
data = master_no_wgd_cancer.dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)

data = data[['tp53_group', 'tp53_res_group', 'Overall Survival Status 0/1', 'Overall_Survival_Months']]

ix1 = data['tp53_res_group'] == 'tp53_res'
ix2 = data['tp53_res_group'] == 'no_tp53_res'

T_exp, E_exp = data.loc[ix1, 'Overall_Survival_Months'], data.loc[ix1, 'Overall Survival Status 0/1']
T_con, E_con = data.loc[ix2, 'Overall_Survival_Months'], data.loc[ix2, 'Overall Survival Status 0/1']

results = logrank_test(T_exp, T_con, event_observed_A=E_exp, event_observed_B=E_con)
results.print_summary()
t_0 -1
null_distribution chi squared
degrees_of_freedom 1
test_name logrank_test
test_statistic p
0 44.02 <0.005
In [91]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - Non-WGD Cohort - {}'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(res_group_list[:2], range(len(res_group_list[:2]))):
    data = master_no_wgd_cancer[master_no_wgd_cancer['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
    kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
    kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
plt.show()
In [89]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - WGD Cohort - {}'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()


for group,i in zip(['bi', 'tp53_res'], range(2)):
    data = master_wgd_cancer[master_wgd_cancer['prewgd_tp53_group_1'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
    kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
    kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
plt.show()
In [90]:
from lifelines import KaplanMeierFitter

fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - Non-WGD Cohort - {}'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(group_list, range(len(group_list))):
    data = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
    kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
    kmf.plot_survival_function(color = palette_list[i], ax=ax)
plt.show()

High CCF

In [93]:
master_high = master_ccf[(master_ccf['ccf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]
In [94]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - High CCF'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(res_group_list, range(len(res_group_list))):
    data = master_high[master_high['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
    except: pass
plt.show()


fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - High CCF'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(group_list, range(len(group_list))):
    data = master_high[master_high['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = palette_list[i], ax=ax)
    except: pass
plt.show()
In [ ]: